Biometric Based Recognition Systems - An Overview

Ahmed AK. Tahir, Steluta Anghelus


Biometrics technology is gaining an important role in providing solutions to many issues in different applications that require person identification such as forensic sciences, security, finance, border screening, ministries and government offices. It is defined as the technique of analyzing physiological and behavioral traits such as face, fingerprint, iris, retina, voice, signature, etc., for person identification and authorization. At present, a lot of research work is being carried out to accomplish biometric recognition systems based on different types of human traits. To provide a comprehensive survey, this paper provides an overview of six biometric traits (iris, finger vein, fingerprint, face, voice and signature). The overview will cover acquisition method, preprocessing methods, features extraction methods, classification methods, application area, system evaluation and strength/weakness.

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G. Kaur, and C. K. Verma, “Comparative Analysis of Biometric Modalities”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 4, issue 4, 2014, pp. 603-613.

D. Fronitasari, Basari, and D. Gunawan,” Palm Vein Feature Extraction Method by Using Optimized DVH Local Binary Pattern”, International Journal of Computer Science and Information Security (IJCSIS), Vol. 17, No. 5, 2019, pp. 8-12.

T. Sabhanayagam, V. P. Venkatesan, and K. Senthamaraikannan, “A Comprehensive Survey on Various Biometric Systems”, International Journal of Applied Engineering Research, Vol. 13, No. 5, 2018, pp. 2276-2297

A. Achban, J. Y. Sari, and Sutardi, “The Implementation of Local Binary Patterns for Biometrics System Based on Dorsal Hand Vein Image “, Indonesian Journal of Information Technology, online ISSN 2599-295, Vol. 2, Issue 2, 2018, Pag. 18-26.

C. B. Tatepamulwar, and V. P. Pawar, “Comparison of Biometric Trends Based on Different Criteria”, Asian Journal of Management Sciences Vol. 2, No. 3, Special Issue, 2014, Pp. 159-165.

J. Galbally, S. Marcel, and J. Fierrez, “Image Quality Assessment for Fake Biometric Detection: Application to Iris, Fingerprint, and Face Recognition”, IEEE Transactions on Image Processing, Vol. 23, No. 2, 2014, pp. 710-724.

H. Srivastava, “A Comparison Based Study on Biometrics for Human Recognition”, Journal of Computer Engineering, Vol. 15, Issue 1, 2013, pp. 22-29.

M. Guermoui and M. L. Mekhalfi, “A sparse representation of complete local binary pattern histogram for human face recognition”, 2016 pp. 1–4, arXiv:org/abs/1605.09584v1.

M. Sarfraz and N. Alfialy, “Introductory Chapter: On Biometrics with Iris”, Recent Advances in Biometrics, IntechOpen, edited by Muhammad Sarfraz, OPEN ACCESS PEER-REVIEWED CHAPTER, 2022, pp. 1-22, doi:10.5772/intechopen.105134.

M. Sharma and H. Elmiligi, “Behavioral Biometrics: Past, Present and Future”, Recent Advances in Biometrics”, IntechOpen, edited by Muhammad Sarfraz, OPEN ACCESS PEER-REVIEWED CHAPTER, 2022, pp. 1-20, doi:10.5772/intechopen.102841.

J. Phillips, K. Bowyer, W. Transactions, and P. J. Flynn, “Comments on The CASIA Version 1.0 Iris Data Set”, IEEE on Pattern Analysis and Machine Intelligence, Vol. 29, No. 10, 2007, pp. 1869-1870.

R. Saini., and N. Rana, “Comparison Of Various Biometric Methods”, International Journal of Advances in Science and Technology (IJAST) Vol. 2, Issue 1, 2014, pp. 24-30.

C. Otti, “Comparison of Biometric Identification Methods”, 11th IEEE International Symposium on Applied Computational Intelligence and Informatics, May 12-14, 2016, Timişoara, Romania, pp. 339-344.

P. Sareen, “Biometrics – Introduction, Characteristics, Basic technique, its Types and Various Performance Measures”, International Journal of Emerging Research in Management &Technology, Vol. 3, Issue 4, 2014, pp. 109-119.

Yadav A. K., and S. K. Grewal, “A Comparative Study of Different Biometric Technologies”, IJCSC, Vol. 5, No. 1, 2014, pp. 37-42.

K. Mali, and S. Bhattacharya, “Comparative Study of Different Biometric Features”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 2, Issue 7, 2013, pp. 2776-2784.

Y. Yin, L. Liu, and X. Sun, “SDUMLA-HMT: A multimodal Biometric Database’, In Biometric Recognition by (Sun, Z., L., J., Chen, X., Tan, T. (Eds.)), Springer Berlin Heidelberg, 2011, pp. 260-268.

M. Vanoni, P. Tome, L. El Shafey, and S. Marcel, “Cross-Database Evaluation Using an Open Finger-vein Sensor”, IEEE Workshop on Biometric Measurements and Systems for Security and Medical Applications (BIOMS) Proceedings, Rome, 2014, pp. 30-35.

Y. Lu, S. J. Xie, S. Yoon, Z. Wang, and D. S. Park, “An Available Database for the Research of Finger-vein Recognition”, the 6th International Congress on Image and Signal Processing, 2013, pp. 410-415.

H. Proença, L. A. Alexandre, “UBIRIS: A Noisy Iris Image Database”, In: Roli, F., Vitulano, S. (eds) Image Analysis and Processing – ICIAP 2005. ICIAP 2005. Lecture Notes in Computer Science, Vol 3617, 2005, Springer, Berlin, Heidelberg, pp. 970-977,

H. Proença, S. Filipe, R. Santos, J. Oliveira, and L. A. Alexandre, “The UBIRIS.v2: a database of visible wavelength images captured on-the-move and at-a-distance”, IEEE Trans Pattern Anal Mach Intell 32(8), 2010, pp. 1529–1535.

A. Kumar and Y. Zhou, “Human identification using finger images,” IEEE Transactions on Image Processing, Vol. 21, No. 4, pp. 2228–2244, 2012.

CASIA-Fingerprints, “Note on CASIA-FingerprintV6”, 2023, pp. 1-70,, Visiting date: 01/30/2023.

E. Fedorov, T. Utkina, and T. Neskorodeva, ”A Voice Signal Filtering Methods for Speaker Biometric Identification”, Books: Recent Advances in Biometrics, IntchOpen, edited by Muhammad Sarfraz, OPEN ACCESS PEER-REVIEWED CHAPTER , 2022, pp. 1-29, doi: 10.5772/intechopen.101975.

Y. M. Al-Omari, S. N. H. S. Abdullah, and K. Omar, “State-Of-The-Art In Offline Signature Verification System”, International Conference on Pattern Analysis And Intelligence Robotics (Vol. 1, 2011, pp. 59-64). Ieee.

N. H. Al-Banhawy, H. Mohsen, N. I. Ghali, “Signature identification and verification systems: a comparative study on the online and offline techniques”, Future Computing and Informatics Journal, Vol. 5, issue 1, 2020, pp. 28-45.

R. Tolosana, R. Vera-Rodriguez et al., “SVC-onGoing: Signature Verification Competition”, Pattern Recognition Vol. 127, No. 5:108609, 2022, pp. 1-14,

M. H. Sigari, M. R. Pourshahabi, and H. R. Pourreza, “Offline Handwritten Signature Identification and Verification Using Multi-Resolution Gabor Wavelet”, International Journal of Biometrics and Bioinformatics, Vol. 5, No. 4, 2011, pp. 234-248.

R. Kumar, J. D. Sharma, and B. Chanda, “Writer-Independent Off-Line Signature Verification Using Surroundedness Feature”, Pattern Recognition Letters, Vol. 33, No. 3, 2012, pp. 301-308.

S. Sthapak, M. Khopade, and C. Kashid, “Artificial Neural Network Based Signature Recognition & Verification”, International Journal of Emerging Technology and Advanced Engineering, Vol. 2, No. 8, 2013, pp. 191-197.

P. Babita, “Online Signature Recognition Using Neural Network”, Journal Of Electrical & Electronics, Vol. 4, No. 3, 2015, pp. 1-5,

doi: 10.4172/2332-0796.1000155.

Z. Chen, X. Xia, and F. Luan, “August). Automatic Online Signature Verification Based on Dynamic Function Features”, In 2016 7th Ieee International Conference on Software Engineering and Service Science (Icsess), pp. 964-968, Ieee.

S. Dey, A. Dutta, J. I. Toledo, S. K. Ghosh, J. Lladós, and U. Pal, “Signet: Convolutional Siamese Network for Writer Independent Offline Signature Verification”, Arxiv Preprint 2017, Arxiv:1707.02131.

S. Kurnaz, and A. Al-Khdhairi, "Offline Signature Identification System to Retrieve Personal Information from Cloud." Journal of Computer Engineering (IOSR-JCE), Volume 20, Issue 1, 2018, pp 56-64.

N. Çalik, O. C. Kurban, A. R. Yilmaz, T. Yildirim, and L. D. Ata, “Large-Scale Offline Signature Recognition Via Deep Neural Networks and Feature Embedding”, Neurocomputing, Vol. 359, 2019, pp. 1-14,

A. AK. Tahir, and A. I. Bindian, “Localizarea Irisului Pentru Sistemul Biometric De Identificare A Ersoanelor”, The XVIII International Conference on Multidisciplinary, "Professor Dorin Paul - Romanian hydropower founder", June-2016 vol. 30/2016, AGIR Edition, ISSN 2067-7138, pp. 215-224. (Conference Proceedings in Romainan and English Languages).

A. AK. Tahir, and S. Anghelus, “A New Method of Eyelid Detection for Iris Recognition System”, The XVIII International Conference on Multidisciplinary, "Professor Dorin Paul - Romanian hydropower founder", June-2018, Cluj, Romania, Vol. 33/2018, ISSN 2067-7138, e-ISSN 2359-828X, 2018, pp. 171-184.

A. AK. Tahir, and S. Anghelus, “An accurate and fast method for eyelid detection” International Journal of Biometrics (IJBM), Vol. 12, No. 2, 2020, pp. 163-178, doi: 10.1504/IJBM.2020.107715.

S. Brindha, “Finger-vein recognition”, International Research Journal of Engineering and Technology (IRJET), Vol. 4, No. 4, 2017, pp. 1298-1300.

A. A. Mustafa, and A. AK. Tahir, “Improving the Performance of Finger vein Recognition System Using a New Scheme of Modified Preprocessing Methods”, Academic Journal of Nawroz University (AJNU), Vol. 9, No. 3, 2020, pp. 397-409,

R. Bansal, P. Sehgal and P. Bedi “Minutiae Extraction from Fingerprint Images - a Review” IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 5, No 3, 2011, pp. 74-85

S. A. Mahmood and A. I. Melhum “An Authentication System Using Fingerprint Minutiae Extraction and Neural Network”, Journal of Al-Nahrain University, Vol.13, No. 4, 2010, pp.216-220

F. Y. Shih, S. Cheng and C. Chuang, “Extracting Faces and Facial Features from Color Images”, International Journal of Pattern Recognition and Artificial Intelligence, Vol. 22, No. 3, 2008, pp. 515–534.

A. AK. Tahir, S. S. Dawood and S. Anghelus, “An Iris Recognition System Based on A New Method of Iris Localization, International Journal of Open Information Technologies, Vol. 9, No. 7, 2021, pp. 67-76.

W. Chen, K. Chih, S. Shih, and C. Hsieh, “Personal identification technique based on human iris recognition with wavelet transform”, Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, (ICASSP ‘05), 2005, pp.949–952.

X. Liu, K. W. Bowyer, and P. J. Flynn, “Experiments with an improved iris segmentation algorithm”, Proceedings of the Fourth IEEE Workshop on Automatic Identification Advanced Technologies, Buffalo, NY, USA, AutoID 2005, pp.118–123.

Z. He, T. Tan, Z. Sun, and X. Qiu, “Robust eyelid, eyelash and shadow localization for iris recognition”, Proceedings of the 15th IEEE International Conference on Image Processing, (ICIP), 2008, pp.265–268.

T. Tan, Z. He, and Z. Sun, “Efficient and robust segmentation of noisy iris images for non-cooperative iris recognition”, Image and Vision Computing, Vol. 28, No. 2, 2010, pp.223–230.

H. Rai, and A. Yadav, “Iris recognition using combined support vector machine and hamming distance approach”, Expert Systems with Applications, Vol. 41, No. 2, 2013, pp.588–593, Elsevier.

F. He, Y. Liu, X. Zhu, C. Huang, Y. Han, and H. Dong, “Multiple local feature representations and their fusion based on an SVR model for iris recognition using optimized Gabor filters”, EURASIP Journal on Advances in Signal Processing, 2014, doi: 10.1186/1687-6180-2014-95.

A. M. Wagh, and S. R. Todmal, “Eyelids, eyelashes detection algorithm and Hough transform method for noise removal in iris recognition”, International Journal of Computer Applications, Vol. 112, No. 3, 2015, pp.28–31.

A. A. Mustafa and A. AK. Tahir, A new finger-vein recognition system using the complete local binary pattern and the phase only correlation, Int. J. Adv. Sig. Img. Sci., Vol. 7, No. 1, 2021, pp. 38-56,

A. AK. Tahir and A. A. Mustafa, “Improving the Performance of Finger Vein Recognition Using the Local Histogram Concatenation of Image Descriptors”, International Journal of Pattern Recognition and Artificial Intelligence (IJPRAI), Vol. 36, No. 14, 2022, pp. 2256020-1-2256020-22,

doi: 10.1142/S0218001422560201.

G.A. Bahgat, A.H. Khalil, N.S. Abdel Kader, S. Mashali, “Fast and accurate algorithm for core point detection in fingerprint images”, Egyptian Informatics Journal, Volume 14, Issue 1, 2013, pp. 15-25,


E. N. Zois, D. Tsourounis, I. Theodorakopoulos, A. L. Kesidis, and G. Economou, “A Comprehensive Study of Sparse Representation Techniques for Offline Signature Verification”, IEEE Transactions on Biometrics, Behavior, and Identity Science, Vol. 1, No. 1, 2019, pp. 68–81.

R. P. Wildes, “Iris Recognition: An Emerging Biometric Technology”, Proceedings of the IEEE, Vol. 85, No. 9, 1997, pp. 1348-1363.

J. Daugman, “New Methods in Iris Recognition,” IEEE Transactions on Systems, Man, And Cybernetics—Part B: Cybernetics, Vol. 37, No. 5, 2007, pp. 1167-1175.

A. AK. Tahir and S. Anghelus, “Improving iris recognition accuracy using Gabor kernels with near-horizontal orientations”, Int. J. Adv. Sig. Img. Sci., Vol. 8, No. 1, 2022, pp. 25–39,

KR. Moses, P. Higgins, M. McCabe, S. Prabhakar, and S. Swann, “Automated Fingerprint Identification System (AFIS)”, In: McRoberts A, editor. The Fingerprint Sourcebook. Washington, DC, USA: U.S. Department of Justice, National Institute of Justice; 2011. pp. 1–33.

M. Iqtait, F. S. Mohamad, M. Mamat, “Feature extraction for face recognition via Active Shape Model (ASM) and Active Appearance Model (AAM)”, IOP Conf. Series: Materials Science and Engineering Vol. 332: 012032, 2018, doi:10.1088/1757-899X/332/1/012032.

S. Ahmed, M. Frikha, T. D. H. Hussein, and J. Rahebi, “Optimum Feature Selection with Particle Swarm Optimization to Face recognition System Using Gabor Wavelet Transform and Deep Learning”, BioMed Research International, Vol. 2021, Article ID 6621540, 2021, pp. 1-13,

O. Tadmor, Y.Wexler, T. Rosenwein, S. Shalev-Shwartz, and A. Shashua, “Learning a metric embedding for face recognition using the multibatch method,” arXiv preprint 2016, arXiv:1605.07270

O. Mamyrbayev, N. Mekebayev, M. Turdalyuly, N. T. Oshanova, T. I. Medeni, and A. Yessentay, “Voice Identification Using Classification Algorithms”, Intelligent System and Computing, Yang Yi Ed. IntechOpen,2020 doi:10.5772/intechopen.88239.

M. Saleem and B. Kovari, “Online signature verification based on signer dependent sampling frequency and dynamic time warping,” in Proceedings of the 2020 7th International Conference on Soft Computing & Machine Intelligence (ISCMI), pp. 182–186, Stockholm, Sweden, November 2020.

Y. Zhou, J. Zheng, H. Hu, and Y. Wang, “Handwritten signature verification method based on improved combined features”, Applied Sciences, Vol. 11, 5867, 2021, pp. 1-14, 10.3390/app11135867.

M. Fayyaz, M. H. Saffar, M. Sabokrou, M. Hoseini, and M. Fathy, “Online signature verification based on feature representation”, Proceedings of the International Symposium on Artificial Intelligence and Signal Processing (AISP), 2015, pp. 211–216, Mashhad, Iran, March 2015.

A. Beresneva, A. Epishkina, and D. Shingalova, “Handwritten signature attributes for its verification,” in Proceedings of the 2018 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), pp. 1477–1480, Petersburg, Russia, January 2018.

Z. Hashim, H. M. Ahmed, and A. H. Alkhayyat, “A Comparative Study among Handwritten Signature Verification Methods Using Machine Learning Techniques”, Scientific Programming Volume 2022, Article ID 8170424, 2022, pp. 1-17,

M. B. Ashwini, I. Mohammad and A. Fawaz, “Evaluation of Iris Recognition System on Multiple Feature Extraction Algorithms and its Combinations”, International Journal of Computer Applications Technology and Research, Vol. 4, No. 8, 2015, pp. 592 – 598.

S. Salve, “Iris Recognition Using Wavelet Transform and SVM Based Approach”, Asian Journal of Convergence in Technology, Vol. V, No. I, 2019, pp. 1-9.

A. Pokhriyal, and S. Lehri, “A NewMethod of Fingerprint Authentication Using2D Wavelets”, Journal of Theoretical and Applied Information Technology, Vol.13, No.2, 2010, pp.131-138

K. Daqrouq, H. Sweidan, A. Balamesh, and M. Ajour, “Off-Line Handwritten Signature Recognition by Wavelet Entropy and Neural Network”, Vol. 19, No. 6, 2017, pp. 1-20, doi: 10.3390/e19060252.

M. R. Nilchiyan, and R. B. Yusof, “Improved Wavelet-Based Online Signature Verification Scheme Considering Pen Scenario Information”, In 2013 1st International Conference on Artificial Intelligence, Modelling and Simulation, 2013, pp. 8-13, Ieee.

M. Tahir, and M. U. Akram, “Online Signature Verification Using Hybrid Features”. In 2015 Second International Conference on Information Security and Cyber Forensics (Infosec), 2015, pp. 11-16, Ieee.

M. R. Pourshahabi, M. H. Sigari, and H.R. Pourreza, “Offline Handwritten Signature Identification and Verification Using Contourlet Transform”, In 2009 International Conference of Soft Computing and Pattern Recognition, 2009, pp. 670-673, Ieee.

S. Y. Ooi, A. B. J. Teoh, Y. H. Pang, and B. Y. Hiew, “Image-Based Handwritten Signature Verification Using Hybrid Methods of Discrete Radon Transform, Principal Component Analysis and Probabilistic Neural Network”, Applied Soft Computing, Vol. 40, 2016, pp. 274-282,

A. A. Abdelrahaman, and M. A. Abdallah, “K-Nearest Neighbor Classifier for Signature Verification System”, In 2013 International Conference on Computing, Electrical and Electronic Engineering (ICCEEE), 2013, pp. 58-62, Ieee.

A. I. Mohammed and A. AK. Tahir, A new image classification system using deep convolution neural network and modified AMSGrad optimizer, J. Univ. Duhok Vol. 22, No. 2, 2019 pp. 89–101,

A. I. Mohammed and A. AK. Tahir, A new optimizer for image classification using Wide ResNet (WRN), Academic. J. Nawroz University (AJNU), Vol. 9, No. 4, 2020, 1–13,

A.S. Al-Waisy R. Qahwaji S. Ipson, et al., “A multi biometric iris recognition system based on a deep learning approach,” Pattern Analysis and Applications, Vol. 21, Issue. 3, 2017, pp. 783–802,

Y. W. Lee, K. W. Kim, T. M. Hoang, et al., “Deep Residual CNN-Based Ocular Recognition Based on Rough Pupil Detection in the Images by NIR Camera Senso,” Sensors, Vol. 19, No. 4, 842, 2019, pp. 1-30,

H. G. Hong, M. B. Lee and K. R. Park, “Convolutional Neural Network-based finger vein recognition using NIR image”, Sensors Vol. 17, No. 6, 2017,

doi: org/10.3390/s17061297.

S. Tang, S. Zhou, W. Kang, Q. Wu, and F. Deng, “Finger vein verification using a Siamese convolutional neural network, IET Biometrics, Vol. 8 Issue. 5, 2019, pp. 306-315, doi: 10.1049/iet-bmt.2018.5245.

R. Das, E.Piciucco, E.Maiorana, and P.Campisi, “Convolutional neural network for finger vein based biometric identification”, in IEEE Trans. Inform. Forensics Sec. Vol. 14, No. 2, 2019, pp. 360–373.

V. Pawar and M. Zaveri, "Graph based K-nearest neighbor minutiae clustering for fingerprint recognition," 2014 10th International Conference on Natural Computation (ICNC), Xiamen, China, 2014, pp. 675-680,

doi: 10.1109/ICNC.2014.6975917.

N. Kanjan, K. Patil, S. Ranaware, and P. Sarokte, “A Comparative

Study of Fingerprint Matching Algorithms”, International Research Journal of Engineering and Technology (IRJET), Vol. 4, Issue 11, 2017, pp. 1892-1896.

T. H. Fuad, A. A. Fime, D. Sikder, A. R. Iftee, J. Rabbi, M. S. Al-Rakhami, A. Gumaei, O. Sen, M. Fuad, And N. Islam, “Recent Advances in Deep Learning Techniques for Face Recognition”, IEEE Access, Vol.9, 2021, pp. 99112-99142,

M. Kirby and L. Sirovish, "Application of the Karhunen-Love procedure for the characterization of human faces", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 12, No. 1, 1990, pp 103-108.

M. Turk and A.P. Pentland, "Eigenfaces for recognition", Journal of Cognitive Neuroscience, Vol. 3, No. 1, 1991, pp 71-86,

L. Muda, M. Begam, and I. Elamvazuthi, “Voice recognition algorithms using Mel frequency cepstral coefficient (MFCC) and dynamic time warping (DTW) techniques”, ArXiv preprint, 2010, arXiv:1003.4083.

S. Yella, N. Gupta, and M. Dougherty, “Comparison of pattern recognition techniques for the classification of impact acoustic emissions”, Transportation Research Part C: Emerging Technologies, Vol. 15, No. 6, 2007, pp. 345-360.

S. Hazmoune, F. Bougamouza, S. Mazouzi, “A new hybrid framework based on hidden Markov models and K-nearest neighbors for speech recognition”, International Journal of Speech Technology, Vol. 21, No. 3, 2018, pp. 689-704,

J. Fierrez, J. Ortega-Garcia, D. Ramos, and J. Gonzalez-Rodriguez,Hmm- “Based On-Line Signature Verification: Feature Extraction and Signature Modeling”, Pattern Recognition Letters, Vol. 28, No. 16, 2007, pp. 2325-2334.

S. Sanda, and S. Amirisetti, “Online Handwritten Signature Verification System: Using Gaussian Mixture Model and Longest Common Sub-Sequences”, Master Thesis, Electrical Engineering, 2017, Pages (40).


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